mPFC: Checking for duplicates with dupRadar
Libraries required
library(dupRadar)
library(biomaRt)
library(parallel)
library(viridis)Loading required package: viridisLiteBam files (marked duplicates)
bamFiles <- list.files(path = "./input", pattern = ".bam$", full.names = T)
names(bamFiles) <- as.character(unlist(sapply(bamFiles, function(x) strsplit(x, "\\.|\\/")[[1]][4])))Analyze duplicates
dm <- NULL
if (!file.exists("./output/analyzedDuplicates.rds")) {
dm <- lapply(bamFiles, function(x) {
a <- analyzeDuprates(bam = x, gtf = "./input/gencode.vM18.chr_patch_hapl_scaff.annotation.gff3",
stranded = 0, paired = F, threads = detectCores() - 1)
# removing versions from gene IDs
a$ID <- as.character(sapply(as.character(a$ID), function(y) strsplit(y, "\\.")[[1]][1]))
return(a)
})
saveRDS(object = dm, file = "./output/analyzedDuplicates.rds")
} else {
dm <- readRDS("./output/analyzedDuplicates.rds")
}Plotting and interpretation
The number of reads per base assigned to a gene in an ideal RNA-Seq data set is expected to be proportional to the abundance of its transcripts in the sample. For lowly expressed genes we expect read duplication to happen rarely by chance, while for highly expressed genes - depending on the total sequencing depth - we expect read duplication to happen often.
A good way to learn if a dataset is following this trend is by relating the normalized number of counts per gene (RPK, as a quantification of the gene expression) and the fraction represented by duplicated reads.
A duprate plot (blue cloud)
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
duprateExpDensPlot(DupMat = dm[[i]], pal = viridis(n = 1000), main = name)
cat("\n \n")
}CTRL_S1_L001
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Duprate Boxplot
The duprateExpBoxplot plot shows the range of the duplication rates at 5% bins (default) along the distribution of RPK gene counts. The x-axis displays the quantile of the RPK distribution, and the average RPK of the genes contained in this quantile.
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
duprateExpBoxplot(DupMat = dm[[i]], main = name)
cat("\n \n")
}CTRL_S1_L001
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Read counts expression
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
readcountExpBoxplot(DupMat = dm[[i]])
cat("\n \n")
}CTRL_S1_L001
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Read counts expression histogram
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
expressionHist(DupMat = dm[[i]])
cat("\n \n")
}CTRL_S1_L001
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Comparison of multi-mapping RPK and uniquely-mapping RPK
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
plot(log2(dm[[i]]$RPK), log2(dm[[i]]$RPKMulti), xlab = "Reads per kb (uniquely mapping reads only)",
ylab = "Reads per kb (all including multimappers, non-weighted)")
cat("\n \n")
}CTRL_S1_L001
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Connection between possible PCR artefacts and GC content
## set up biomart connection for mouse (needs internet connection)
ensm <- useMart("ensembl")
ensm <- useDataset("mmusculus_gene_ensembl", mart = ensm)
## get a table which has the gene GC content for the IDs that have been used to
## generate the table
tr <- getBM(attributes = c("ensembl_gene_id", "percentage_gene_gc_content"), values = TRUE,
mart = ensm)
## create a GC vector with IDs as element names
mgi.gc <- tr$percentage_gene_gc_content
names(mgi.gc) <- tr$ensembl_gene_idCompare the dependence of duplication rate on expression level independently for below and above median GC genes
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
keep <- dm[[i]]$ID %in% tr$ensembl_gene_id
dm.gc <- dm[[i]][keep, ]
dm.gc$gc <- mgi.gc[dm.gc$ID]
par(mfrow = c(1, 2))
## below median GC genes
duprateExpDensPlot(dm.gc[dm.gc$gc <= 45, ], main = "below median GC genes")
## above median GC genes
duprateExpDensPlot(dm.gc[dm.gc$gc >= 45, ], main = "above median GC genes")
cat("\n \n")
}CTRL_S1_L001
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References
report::cite_packages(sessionInfo())SessionInfo
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 3.6.2 (2019-12-12)
os Ubuntu 16.04.7 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Zurich
date 2020-10-07
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